OpenRaiser/Intern-Atlas huggingface dataset:
Swarm Analytics: Designing Information Markers to Characterise Swarm Systems in Shepherding Contexts
Venue: arXiv cs.AI 2022
Year: 2022

Contemporary swarm indicators are often used in isolation, focused on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members, and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about swarms into an ontologically-arranged collection of information markers to characterise the swarm from the perspective of an external observer\textemdash, a recognition agent. Our contribution shows the foundations for a new area of research that we tile swarm analytics, whose primary concern is with the design and organisation of collections of swarm markers to understand, detect, recognise, track, and learn a particular insight about a swarm system. We present our designed framework of information markers that offer a new avenue for swarm research, especially for heterogeneous and cognitive swarms that may require more advanced capabilities to detect agencies and categorise agent influences and responses.
Music Separation Enhancement with Generative Modeling
Venue: arXiv cs.SD 2022
Year: 2022

Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous noise or removing harmonics. We propose a post-processing model (the Make it Sound Good (MSG) post-processor) to enhance the output of music source separation systems. We apply our post-processing model to state-of-the-art waveform-based and spectrogram-based music source separators, including a separator unseen by MSG during training. Our analysis of the errors produced by source separators shows that waveform models tend to introduce more high-frequency noise, while spectrogram models tend to lose transients and high frequency content. We introduce objective measures to quantify both kinds of errors and show MSG improves the source reconstruction of both kinds of errors. Crowdsourced subjective evaluations demonstrate that human listeners prefer source estimates of bass and drums that have been post-processed by MSG.
User-Controllable Latent Transformer for StyleGAN Image Layout Editing
Venue: arXiv cs.CV 2022
Year: 2022

Latent space exploration is a technique that discovers interpretable latent directions and manipulates latent codes to edit various attributes in images generated by generative adversarial networks (GANs). However, in previous work, spatial control is limited to simple transformations (e.g., translation and rotation), and it is laborious to identify appropriate latent directions and adjust their parameters. In this paper, we tackle the problem of editing the StyleGAN image layout by annotating the image directly. To do so, we propose an interactive framework for manipulating latent codes in accordance with the user inputs. In our framework, the user annotates a StyleGAN image with locations they want to move or not and specifies a movement direction by mouse dragging. From these user inputs and initial latent codes, our latent transformer based on a transformer encoder-decoder architecture estimates the output latent codes, which are fed to the StyleGAN generator to obtain a result image. To train our latent transformer, we utilize synthetic data and pseudo-user inputs generated by off-the-shelf StyleGAN and optical flow models, without manual supervision. Quantitative and qualitative evaluations demonstrate the effectiveness of our method over existing methods.
Computing Clique Cover with Structural Parameterization
Venue: arXiv cs.DS 2022
Year: 2022

An abundance of real-world problems manifest as covering edges and/or vertices of a graph with cliques that are optimized for some objectives. We consider different structural parameters of graph, and design fixed-parameter tractable algorithms for a number of clique cover problems. Using a set representation of graph, we introduce a framework for computing clique cover with different objectives. We demonstrate use of the framework for a variety of clique cover problems. Our results include a number of new algorithms with exponential to double exponential improvements in the running time.
Learning From Positive and Unlabeled Data Using Observer-GAN
Venue: arXiv cs.CV 2022
Year: 2022

The problem of learning from positive and unlabeled data (A.K.A. PU learning) has been studied in a binary (i.e., positive versus negative) classification setting, where the input data consist of (1) observations from the positive class and their corresponding labels, (2) unlabeled observations from both positive and negative classes. Generative Adversarial Networks (GANs) have been used to reduce the problem to the supervised setting with the advantage that supervised learning has state-of-the-art accuracy in classification tasks. In order to generate \textit{pseudo}-negative observations, GANs are trained on positive and unlabeled observations with a modified loss. Using both positive and \textit{pseudo}-negative observations leads to a supervised learning setting. The generation of pseudo-negative observations that are realistic enough to replace missing negative class samples is a bottleneck for current GAN-based algorithms. By including an additional classifier into the GAN architecture, we provide a novel GAN-based approach. In our suggested method, the GAN discriminator instructs the generator only to produce samples that fall into the unlabeled data distribution, while a second classifier (observer) network monitors the GAN training to: (i) prevent the generated samples from falling into the positive distribution; and (ii) learn the features that are the key distinction between the positive and negative observations. Experiments on four image datasets demonstrate that our trained observer network performs better than existing techniques in discriminating between real unseen positive and negative samples.
Nearest Neighbor Non-autoregressive Text Generation
Venue: arXiv cs.CL 2022
Year: 2022

Non-autoregressive (NAR) models can generate sentences with less computation than autoregressive models but sacrifice generation quality. Previous studies addressed this issue through iterative decoding. This study proposes using nearest neighbors as the initial state of an NAR decoder and editing them iteratively. We present a novel training strategy to learn the edit operations on neighbors to improve NAR text generation. Experimental results show that the proposed method (NeighborEdit) achieves higher translation quality (1.69 points higher than the vanilla Transformer) with fewer decoding iterations (one-eighteenth fewer iterations) on the JRC-Acquis En-De dataset, the common benchmark dataset for machine translation using nearest neighbors. We also confirm the effectiveness of the proposed method on a data-to-text task (WikiBio). In addition, the proposed method outperforms an NAR baseline on the WMT'14 En-De dataset. We also report analysis on neighbor examples used in the proposed method.
Understanding Deep Learning using Topological Dynamical Systems, Index Theory, and Homology
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we investigate Deep Learning Models using topological dynamical systems, index theory, and computational homology. These mathematical machinery was invented initially by Henri Poincare around 1900 and developed over time to understand shapes and dynamical systems whose structure and behavior is too complicated to solve for analytically but can be understood via global relationships. In particular, we show how individual neurons in a neural network can correspond to simplexes in a simplicial complex manifold approximation to the decision surface learned by the NN, and how these simplexes can be used to compute topological invariants from algebraic topology for the decision manifold with an explicit computation of homology groups by hand in a simple case. We also show how the gradient of the probability density function learned by the NN creates a dynamical system, which can be analyzed by a myriad of topological tools such as Conley Index Theory, Morse Theory, and Stable Manifolds. We solve analytically for associated the differential equation for a trained NN with a single hidden layer of 256 Neurons applied to the MINST digit dataset, and approximately numerically that it a sink and basin of attraction for each of the 10 classes, but the sinks and strong attracting manifolds lie in regions not corresponding to images of actual digits. Index theory implies the existence of saddles. Level sets of the probability functions are 783-dimensional manifolds which can only change topology at critical points of the dynamical system, and these changes in topology can be investigated with Morse Theory.
Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?
Venue: arXiv cs.NE 2022
Year: 2022

It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due to "the curse of dimensionality". In tasks for which there is a vast amount of labeled data, Deep Networks seem to solve this issue with many layers and a non-Hebbian backpropagation algorithm. The brain, however, seems to be able to solve the problem with few layers. In this work, we hypothesize that this happens by using Hebbian learning. Actually, the Hebbian-like learning rule of Restricted Boltzmann Machines learns the input patterns asymmetrically. It exclusively learns the correlation between non-zero values and ignores the zeros, which represent the vast majority of the input dimensionality. By ignoring the zeros "the curse of dimensionality" problem can be avoided. To test our hypothesis, we generated several sparse datasets and compared the performance of a Restricted Boltzmann Machine classifier with some Backprop-trained networks. The experiments using these codes confirm our initial intuition as the Restricted Boltzmann Machine shows a good generalization performance, while the Neural Networks trained with the backpropagation algorithm overfit the training data.
Veritas: Answering Causal Queries from Video Streaming Traces
Venue: arXiv cs.NI 2022
Year: 2022

In this paper, we seek to answer what-if questions - i.e., given recorded data of an existing deployed networked system, what would be the performance impact if we changed the design of the system (a task also known as causal inference). We make three contributions. First, we expose the complexity of causal inference in the context of adaptive bit rate video streaming, a challenging domain where the network conditions during the session act as a sequence of latent and confounding variables, and a change at any point in the session has a cascading impact on the rest of the session. Second, we present Veritas, a novel framework that tackles causal reasoning for video streaming without resorting to randomised trials. Integral to Veritas is an easy to interpret domain-specific ML model (an embedded Hidden Markov Model) that relates the latent stochastic process (intrinsic bandwidth that the video session can achieve) to actual observations (download times) while exploiting control variables such as the TCP state (e.g., congestion window) observed at the start of the download of video chunks. We show through experiments on an emulation testbed that Veritas can answer both counterfactual queries (e.g., the performance of a completed video session had it used a different buffer size) and interventional queries (e.g., estimating the download time for every possible video quality choice for the next chunk in a session in progress). In doing so, Veritas achieves accuracy close to an ideal oracle, while significantly outperforming both a commonly used baseline approach, and Fugu (an off-the-shelf neural network) neither of which account for causal effects.
Getting Quechua Closer to Final Users through Knowledge Graphs
Venue: arXiv cs.CY 2022
Year: 2022

Quechua language and Quechua knowledge gather millions of people around the world, especially in several countries in South America. Unfortunately, there are only a few resources available to Quechua communities, and they are mainly stored in PDF format. In this paper, the Quechua Knowledge Graph is envisioned and generated as an effort to get Quechua closer to the Quechua communities, researchers, and technology developers. Currently, there are 553636 triples stored in the Quechua Knowledge Graph, which is accessible on the Web, retrievable by machines, and curated by users. To showcase the deployment of the Quechua Knowledge Graph, use cases and future work are described.
Evaluating virtual laboratory platforms for supporting on-line information security courses
Venue: arXiv cs.CY 2022
Year: 2022

Distance education had existed for a long time, then it has undergone a renaissance with the advent of computers and the Internet. Distance education relied on physically delivered material and assessments to students, who work offline at home. More recently, online learning or e-learning introduced virtual classrooms, assessments, online tests and transformed the classroom an into interactive online classroom. Despite the large number of online degrees offered, face-to-face remained the dominant mode and e-learning was just used to complement the classroom. The Covid-19 pandemic continues to impact higher education, and online learning is a forgone conclusion. However, the digital divide hindered the disadvantaged schools and students efforts to transition to online learning. As the pandemic continues to change the education landscape, many challenges arise and prevent student from realising the full potential of e-learning. One of those is the access to physical labs in science, engineering, and computer science programs. This study evaluates practical solutions for virtual labs to be used in teaching information security and ethical hacking. The course ran over five semesters, and 164 students were surveyed. The survey measured perceptions, enjoyment, experiences and attitudes towards virtual labs, and the results were supporting adoption and acceptance of virtual labs.
COVID Future Panel Survey: A Unique Public Dataset Documenting How U.S. Residents' Travel Related Choices Changed During the COVID-19 Pandemic
Venue: arXiv cs.CY 2022
Year: 2022

The COVID-19 pandemic is an unprecedented global crisis that has impacted virtually everyone. We conducted a nationwide online longitudinal survey in the United States to collect information about the shifts in travel-related behavior and attitudes before, during, and after the pandemic. The survey asked questions about commuting, long distance travel, working from home, online learning, online shopping, pandemic experiences, attitudes, and demographic information. The survey has been deployed to the same respondents thrice to observe how the responses to the pandemic have evolved over time. The first wave of the survey was conducted from April 2020 to June 2021, the second wave from November 2020 to August 2021, and the third wave from October 2021 to November 2021. In total, 9,265 responses were collected in the first wave; of these, 2,877 respondents returned for the second wave and 2,728 for the third wave. Survey data are publicly available. This unique dataset can aid policy makers in making decisions in areas including transport, workforce development, and more. This article demonstrates the framework for conducting this online longitudinal survey. It details the step-by-step procedure involved in conducting the survey and in curating the data to make it representative of the national trends.
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models
Venue: arXiv cs.LG 2022
Year: 2022

The proliferation of artificial intelligence is increasingly dependent on model understanding. Understanding demands both an interpretation - a human reasoning about a model's behavior - and an explanation - a symbolic representation of the functioning of the model. Notwithstanding the imperative of transparency for safety, trust, and acceptance, the opacity of state-of-the-art reinforcement learning algorithms conceals the rudiments of their learned strategies. We have developed a policy regularization method that asserts the global intrinsic affinities of learned strategies. These affinities provide a means of reasoning about a policy's behavior, thus making it inherently interpretable. We have demonstrated our method in personalized prosperity management where individuals' spending behavior in time dictate their investment strategies, i.e. distinct spending personalities may have dissimilar associations with different investment classes. We now explain our model by reproducing the underlying prototypical policies with discretized Markov models. These global surrogates are symbolic representations of the prototypical policies.
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
Venue: arXiv cs.CV 2022
Year: 2022

Automatic fault detection is a major challenge in many sports. In race walking, referees visually judge faults according to the rules. Hence, ensuring objectivity and fairness while judging is important. To address this issue, some studies have attempted to use sensors and machine learning to automatically detect faults. However, there are problems associated with sensor attachments and equipment such as a high-speed camera, which conflict with the visual judgement of referees, and the interpretability of the fault detection models. In this study, we proposed a fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified referees to realize fair fault judgement. We verified them using smartphone videos of normal race walking and walking with intentional faults in several athletes including the medalist of the Tokyo Olympics. The validation results show that the proposed system detected faults with an average accuracy of over 90%. We also revealed that the machine learning model detects faults according to the rules of race walking. In addition, the intentional faulty walking movement of the medalist was different from that of university walkers. This finding informs realization of a more general fault detection model. The code and data are available at https://github.com/SZucchini/racewalk-aijudge.
Towards Robust Drone Vision in the Wild
Venue: arXiv cs.CV 2022
Year: 2022

The past few years have witnessed the burst of drone-based applications where computer vision plays an essential role. However, most public drone-based vision datasets focus on detection and tracking. On the other hand, the performance of most existing image super-resolution methods is sensitive to the dataset, specifically, the degradation model between high-resolution and low-resolution images. In this thesis, we propose the first image super-resolution dataset for drone vision. Image pairs are captured by two cameras on the drone with different focal lengths. We collect data at different altitudes and then propose pre-processing steps to align image pairs. Extensive empirical studies show domain gaps exist among images captured at different altitudes. Meanwhile, the performance of pretrained image super-resolution networks also suffers a drop on our dataset and varies among altitudes. Finally, we propose two methods to build a robust image super-resolution network at different altitudes. The first feeds altitude information into the network through altitude-aware layers. The second uses one-shot learning to quickly adapt the super-resolution model to unknown altitudes. Our results reveal that the proposed methods can efficiently improve the performance of super-resolution networks at varying altitudes.
SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset
Venue: arXiv cs.CL 2022
Year: 2022

As the first session-level Chinese dataset, CHASE contains two separate parts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and 3,456 sessions translated from English SParC (CHASE-T). We find the two parts are highly discrepant and incompatible as training and evaluation data. In this work, we present SeSQL, yet another large-scale session-level text-to-SQL dataset in Chinese, consisting of 5,028 sessions all manually constructed from scratch. In order to guarantee data quality, we adopt an iterative annotation workflow to facilitate intense and in-time review of previous-round natural language (NL) questions and SQL queries. Moreover, by completing all context-dependent NL questions, we obtain 27,012 context-independent question/SQL pairs, allowing SeSQL to be used as the largest dataset for single-round multi-DB text-to-SQL parsing. We conduct benchmark session-level text-to-SQL parsing experiments on SeSQL by employing three competitive session-level parsers, and present detailed analysis.
Software Performability Analysis Using Fast Parametric Model Checking
Venue: arXiv cs.LO 2022
Year: 2022

We present an efficient parametric model checking (PMC) technique for the analysis of software performability, i.e., of the performance and dependability properties of software systems. The new PMC technique works by automatically decomposing a parametric discrete-time Markov chain (pDTMC) model of the software system under verification into fragments that can be analysed independently, yielding results that are then combined to establish the required software performability properties. Our fast parametric model checking (fPMC) technique enables the formal analysis of software systems modelled by pDTMCs that are too complex to be handled by existing PMC methods. Furthermore, for many pDTMCs that state-of-the-art parametric model checkers can analyse, fPMC produces solutions (i.e., algebraic formulae) that are simpler and much faster to evaluate. We show experimentally that adding fPMC to the existing repertoire of PMC methods improves the efficiency of parametric model checking significantly, and extends its applicability to software systems with more complex behaviour than currently possible.
Remote Data Auditing and How it May Affect the Chain of Custody in a Cloud Environment
Venue: arXiv cs.CY 2022
Year: 2022

As big data collection continues to grow, more and more organizations are relying on outsourcing their data to cloud-based environments. This includes the federal government and several agencies that depend on maintaining our citizens' secure data. Law enforcement agencies from the national level down to large city police departments are also using the cloud environment to store data. These agencies see this as a method of securing data while saving money by not maintaining the large data centers required to house this information. This data solution presents in own set of problems in that the outsourced data can become untrustworthy due to the lack of control of the data owners. Cloud computing is facing many difficulties, with security being the primary issue. This is because the cloud computing service provider is a separate entity; any data stored in the cloud can be interpreted as giving up control of the data by the primary data owner. [1] Remote data auditing (RDA) is increasingly important when managing data in a cloud environment, especially when organizations have to store their data in a multi-cloud environment. The challenging security threats posed by attempting to maintain the integrity of the data for proper auditing is a trial that was never addressed in the past.